Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "68"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 68 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 68, Node N03:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459998 dish_maintenance 100.00% 100.00% 0.00% 0.00% - - 22.909825 0.341130 12.394738 0.689316 7.963638 -0.476291 6.061464 -0.518383 0.0315 0.6327 0.5005 nan nan
2459997 dish_maintenance 100.00% 100.00% 0.00% 0.00% - - 25.156114 0.334257 13.136063 0.820229 7.689359 0.209314 10.143453 -0.360841 0.0354 0.6491 0.5235 nan nan
2459996 dish_maintenance 100.00% 100.00% 0.00% 0.00% - - 27.467786 0.104745 16.274819 0.083525 7.233146 -0.444714 3.894323 -0.064034 0.0362 0.6514 0.5245 nan nan
2459995 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 20.498316 29.375005 1.229680 16.186601 3.639325 9.482222 0.047781 6.979467 0.3484 0.0338 0.2493 nan nan
2459994 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 19.655303 28.317270 1.078989 14.179707 4.280499 9.449687 -0.062248 6.783949 0.3498 0.0289 0.2495 nan nan
2459993 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 21.295907 27.972198 1.051361 13.305964 4.524941 10.879174 0.514236 7.349296 0.2850 0.0255 0.1894 nan nan
2459991 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 22.968186 32.582848 0.986152 13.998764 4.463110 10.635834 0.038023 6.650179 0.3672 0.0281 0.2681 nan nan
2459990 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.500753 26.678873 0.955444 13.654266 4.762277 10.944735 0.207153 7.618052 0.3657 0.0306 0.2682 nan nan
2459989 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.534847 27.245345 0.829856 12.416451 4.145352 9.210535 0.135015 5.898780 0.3619 0.0277 0.2627 nan nan
2459988 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 22.084781 31.979131 0.912956 14.071981 5.847770 13.091880 -0.175181 5.839352 0.3644 0.0276 0.2710 nan nan
2459987 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.492557 27.169762 0.910371 13.779504 4.099410 7.875864 1.357575 11.273360 0.3725 0.0309 0.2782 nan nan
2459986 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 22.417870 32.749746 1.057126 14.902780 5.410780 11.133310 2.157410 12.699393 0.4049 0.0291 0.3061 nan nan
2459985 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 21.021551 30.464353 0.906856 13.848856 4.089599 8.513852 0.291743 11.823459 0.3651 0.0288 0.2741 nan nan
2459984 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.768121 29.607294 1.088168 14.320372 7.494556 11.818663 0.816425 7.997760 0.3809 0.0322 0.2900 nan nan
2459983 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 20.149831 28.213086 1.098761 13.660440 5.116929 11.046255 1.442077 10.605368 0.4031 0.0301 0.2951 nan nan
2459982 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 22.735976 21.184562 1.662491 11.744922 2.680847 5.444928 0.578454 3.652608 0.4654 0.0289 0.3535 nan nan
2459981 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.428533 25.678125 1.039217 14.623183 5.543055 12.237272 -0.368250 7.769199 0.3670 0.0309 0.2767 nan nan
2459980 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.327448 25.241077 0.867389 13.282319 5.542078 10.752339 2.407820 6.595871 0.4294 0.0303 0.3233 nan nan
2459979 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 19.228393 26.100498 0.712252 12.524640 4.844377 9.998497 -0.332672 7.184169 0.3594 0.0289 0.2684 nan nan
2459978 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 19.481895 26.575943 0.897862 13.483327 4.855350 10.886084 -0.832388 8.244255 0.3564 0.0269 0.2655 nan nan
2459977 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 18.988512 27.428090 0.971822 13.165888 9.793254 11.184914 -0.481393 8.162589 0.3445 0.0312 0.2590 nan nan
2459976 dish_maintenance 100.00% 0.00% 100.00% 0.00% - - 19.688347 26.700078 1.031660 13.807345 4.155670 10.827662 -0.012208 6.487220 0.3583 0.0280 0.2684 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 68: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance ee Shape 22.909825 22.909825 0.341130 12.394738 0.689316 7.963638 -0.476291 6.061464 -0.518383

Antenna 68: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance ee Shape 25.156114 25.156114 0.334257 13.136063 0.820229 7.689359 0.209314 10.143453 -0.360841

Antenna 68: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance ee Shape 27.467786 27.467786 0.104745 16.274819 0.083525 7.233146 -0.444714 3.894323 -0.064034

Antenna 68: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 29.375005 20.498316 29.375005 1.229680 16.186601 3.639325 9.482222 0.047781 6.979467

Antenna 68: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 28.317270 19.655303 28.317270 1.078989 14.179707 4.280499 9.449687 -0.062248 6.783949

Antenna 68: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 27.972198 21.295907 27.972198 1.051361 13.305964 4.524941 10.879174 0.514236 7.349296

Antenna 68: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 32.582848 22.968186 32.582848 0.986152 13.998764 4.463110 10.635834 0.038023 6.650179

Antenna 68: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 26.678873 26.678873 18.500753 13.654266 0.955444 10.944735 4.762277 7.618052 0.207153

Antenna 68: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 27.245345 27.245345 18.534847 12.416451 0.829856 9.210535 4.145352 5.898780 0.135015

Antenna 68: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 31.979131 31.979131 22.084781 14.071981 0.912956 13.091880 5.847770 5.839352 -0.175181

Antenna 68: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 27.169762 18.492557 27.169762 0.910371 13.779504 4.099410 7.875864 1.357575 11.273360

Antenna 68: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 32.749746 32.749746 22.417870 14.902780 1.057126 11.133310 5.410780 12.699393 2.157410

Antenna 68: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 30.464353 30.464353 21.021551 13.848856 0.906856 8.513852 4.089599 11.823459 0.291743

Antenna 68: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 29.607294 18.768121 29.607294 1.088168 14.320372 7.494556 11.818663 0.816425 7.997760

Antenna 68: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 28.213086 20.149831 28.213086 1.098761 13.660440 5.116929 11.046255 1.442077 10.605368

Antenna 68: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance ee Shape 22.735976 22.735976 21.184562 1.662491 11.744922 2.680847 5.444928 0.578454 3.652608

Antenna 68: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 25.678125 25.678125 18.428533 14.623183 1.039217 12.237272 5.543055 7.769199 -0.368250

Antenna 68: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 25.241077 25.241077 18.327448 13.282319 0.867389 10.752339 5.542078 6.595871 2.407820

Antenna 68: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 26.100498 19.228393 26.100498 0.712252 12.524640 4.844377 9.998497 -0.332672 7.184169

Antenna 68: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 26.575943 26.575943 19.481895 13.483327 0.897862 10.886084 4.855350 8.244255 -0.832388

Antenna 68: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 27.428090 18.988512 27.428090 0.971822 13.165888 9.793254 11.184914 -0.481393 8.162589

Antenna 68: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
68 N03 dish_maintenance nn Shape 26.700078 26.700078 19.688347 13.807345 1.031660 10.827662 4.155670 6.487220 -0.012208
In [ ]: